This is a project I am working on to familiarize myself with single-cell RNA-seq analysis using Python. I am using this repository to practice the single-cell workflow and understand what each step actually does.
I am mainly following YouTube tutorial videos and online courses on Udemy. My main goal is to become familiar with the single-cell analysis workflow in Python, especially because I am also interested in using machine learning with biological data.
This project is mostly about learning the tools and understanding the steps of the analysis and it is not intended to be a finished or publication-ready analysis.
At the moment, the workflow includes:
- Reading 10x Genomics data with Scanpy
- Quality control
- Doublet detection using scVI / SOLO
- Filtering cells
- Finding highly variable genes
- Dimensionality reduction and integration with scVI
- UMAP
- Leiden clustering
- Looking at marker genes
- Basic cell-type annotation
- Pseudobulk analysis
- Differential expression analysis with PyDESeq2
- Python
- Scanpy
- scVI-tools
- Pandas
- NumPy
- Matplotlib
- PyDESeq2
I am using this project mainly to practice the workflow and get more comfortable with single-cell RNA-seq analysis in Python. As I learn more about single-cell methods, I expect to change the analysis and try different approaches.
For now, the main goal is learning rather than producing a final biological result.
I will keep updating this repository as I learn more and improve the analysis.